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We propose a novel methodology to generate domain-specific large-scalequestion answering (QA) datasets by re-purposing existing annotations for otherNLP tasks. We demonstrate an instance of this methodology in generating alarge-scale QA dataset for electronic medical records by leveraging existingexpert annotations on clinical notes for various NLP tasks from the communityshared i2b2 datasets. The resulting corpus (emrQA) has 1 millionquestion-logical form and 400,000+ question-answer evidence pairs. Wecharacterize the dataset and explore its learning potential by trainingbaseline models for question to logical form and question to answer mapping.

emrQA: A Large Corpus for Question Answering on Electronic Medical Records